Active Triggering of Pneumatic Rehabilitation Gloves Based on Surface Electromyography Sensors

Yongfei Feng; Mingwei Zhong; Xusheng Wang; Hao Lu; Hongbo Wang; Pengcheng Liu; Luige Vladareanu

doi:10.7717/peerj-cs.448

Active Triggering of Pneumatic Rehabilitation Gloves Based on Surface Electromyography Sensors

Yongfei Feng, Mingwei Zhong, Xusheng Wang, Hao Lu, Hongbo Wang, Pengcheng Liu, Luige Vladareanu

Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

The portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient's hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human's arm and signal acquisition process was carried out. Then according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. According to individual differences, the corresponding BP neural network model database of different people was established. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient's hand movement. Preliminary tests have confirmed that the device has a high accuracy rate of trend recognition for hand movement. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.

Original language	English
Article number	e448
Number of pages	19
Journal	PeerJ Computer Science
Volume	7
DOIs	https://doi.org/10.7717/peerj-cs.448
Publication status	Published - 19 Apr 2021

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10.7717/peerj-cs.448Licence: CC BY

Active triggering control of pneumatic rehabilitation gloves based on surface electromyography sensors
© 2021 Feng et al.
Final published version, 5.4 MBLicence: CC BY

Cite this

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title = "Active Triggering of Pneumatic Rehabilitation Gloves Based on Surface Electromyography Sensors",

abstract = "The portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient's hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human's arm and signal acquisition process was carried out. Then according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. According to individual differences, the corresponding BP neural network model database of different people was established. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient's hand movement. Preliminary tests have confirmed that the device has a high accuracy rate of trend recognition for hand movement. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.",

author = "Yongfei Feng and Mingwei Zhong and Xusheng Wang and Hao Lu and Hongbo Wang and Pengcheng Liu and Luige Vladareanu",

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AU - Feng, Yongfei

AU - Zhong, Mingwei

AU - Wang, Xusheng

AU - Lu, Hao

AU - Wang, Hongbo

AU - Liu, Pengcheng

AU - Vladareanu, Luige

PY - 2021/4/19

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AB - The portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient's hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human's arm and signal acquisition process was carried out. Then according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. According to individual differences, the corresponding BP neural network model database of different people was established. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient's hand movement. Preliminary tests have confirmed that the device has a high accuracy rate of trend recognition for hand movement. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.

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